Global trade drives transboundary transfer of the health impacts of polycyclic aromatic hydrocarbon emissions

International trade leads to a redistribution of pollutant emissions related to the production of goods and services and subsequently affects their severe health impacts. Here, we present a framework of emissions inventories, input-output model, numerical atmospheric chemistry model, and estimates of the global burden of disease. Specifically, we assess emissions and health impacts of polycyclic aromatic hydrocarbons (PAH), a carcinogenic byproduct of production activities, and consider income, production, final sale, and consumption stages of the global supply chain between 2012 and 2015. We find that in 2015, global anthropogenic PAH emissions were 304 Gg (95% CI: 213~421 Gg) and estimated related lifetime lung cancer deaths were 6.9 × 104 (95% CI: 1.8 × 104~1.5 × 105 deaths). The role of trade in driving the PAH-related health risks was greater than that in driving the emissions. Our findings indicate that international cooperation is needed to optimise the global supply chains and mitigate PAH emissions and health impacts.


Supplementary Note 1: Emission factors with technology splits
Sixteen priority PAHs were included in the inventory, which are naphthalene

Supplementary Note 2: Comparison with other PAH inventories
This study estimated the 16PAHs emissions emitted in the world from 2012 to 2015. The emission inventory in this study includes sources of fuel consumption (including coal, natural gas, petroleum, and biomass), and industrial processes (including primary Al production, pig iron production, crude steel production, coke production, petroleum catalytic cracking, and agricultural residues burning). The natural sources were calculated, but not involved in the EE-MRIO model. The PAH emission inventory in 2015 was estimated as 357 Gg (anthropogenic sources: 304 Gg).
Several previous studies estimated the PAH emissions from different sources in different years. Zhang

Supplementary Note 3: Production-, income-, final sale-, and final consumptionbased accountings
This study investigated the influence of international trade on PAH emissions and health impacts from the whole stages of the supply chain (primary input, production, final sale, and final consumption).
Supplementary Figure 1 shows the framework of production-based, income-based, final sale-based, and final consumption-based accounting. There are four kinds of regions in a supply chain (Supplementary Figure 1): region A is the primary supplier whose investment supports the downstream production process, region B is the producer of products with direct emissions, and region C is the final seller of products (or say the final producer), and region D is the final consumer. Regions A, C, and D have no direct emissions, but they all benefit from production processes. Primary suppliers (e.g., supplies of capital and labor forces) receive a payment from the inputs that they supply. Final sellers (producers) profit from the sales of final products. Final consumers benefit from the enjoyment of products and services. Using the income- These accounting methods were based on the multiregional input-output model. (1) The superscripts m, n indicate the region at row and column, and the superscripts i, j represent the sector at row and column.  In addition, the increases in emissions of most regions were caused by declines in energy efficiency and input/sale structure. However, the situation in China was different.

Supplementary
Due to the large economic development, the energy efficiency resulted in a decrease in emissions. Since the large export of products, the input/sale structure and consumption level caused the most increase in emissions of China. Thus, it is essential to improve the economic supply chain through global cooperation for mitigating PAH emissions.
In terms of health impacts, meteorological change was the most significant factor driving the increase in health impacts in most regions. The decrease in health impacts of PAH caused by reducing emissions was offset by meteorological change.
Furthermore, energy efficiency drove the large increases in deaths in India, the rest of Asia, and Sub-Saharan Africa. With the increase in lung cancer rate in the world, the increase in PAH-related deaths was more serious in the world, especially in populous regions. Thus, the mitigation of PAH-related health impacts requires both the improvement of energy efficiency in developing regions and the optimization of the global economic supply chain.

Supplementary Note 5: PAH emission linkages analysis
In this study, emission linkage analysis was used to identify economic sectors' total polluting characteristics based on intersectoral linkages of PAH emissions 3 .
Emission linkage analysis can determine the key PAH emission sectors with the greater potential to reduce PAH emissions through small changes in economic activity 4 , 5 . The generalized backward and forward linkages (BL, FL) can be obtained using the following equations: In the analysis, = ( − ) −1 indicates the total embodied emission intensity, whose element lij can be applied to calculate (1) The compilation of global PAH emission inventory is subject to uncertainty due to incomplete knowledge of fuel consumption, emission factors, and activity rates.
The uncertainty of source-specific PAH emission factors was obtained from previous literature 1 . The uncertainties of fuel consumption and activity rates are not provided in the statistics of the International Energy Agency (IEA). Following the previous study 1 , the various intervals of historical fuel consumption and activity rates were set to be 20% of the means for biomass burning and open fires, 5% for energy production, 30% for waste burning, 15% for ship and aviation and industrial sectors, and 10% for all other sources. Thus, the uncertainty of region-specific production-based PAH emissions can be obtained based on the uncertainties of emission factors, fuel consumption, and activity rates.
(2) The estimation of income-based, final sale-based and consumption-based PAH emissions of worldwide regions share the most uncertainties with production-based PAH emissions and includes an additional uncertainty from the EE-MRIO model associated with the inaccuracies of sectoral mapping, economic statistics, and data harmonization [11][12][13] . A previous study investigated a comparable variability between production-based and consumption-based CO2 emissions across studies using different MRIO models and reported that MRIO-related uncertainty was relatively smaller than the uncertainty of production-based emissions 14 . According to previous study 14 Table 12).